Surface reflectivity climatologies from UV to NIR determined from Earth observations by GOME‐2 and SCIAMACHY

Tilstra, L. G.; Tuinder, O. N. E.; Wang, P.; Stammes, P.

doi:10.1002/2016jd025940

Cited by 99 publications

(149 citation statements)

References 60 publications

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“…Surface reflectance at the lower accuracy sites in V2, such as at Chiba University, Kobe, Xinglong, and Osaka, is brighter (urban surfaces) than at other sites, and the current identification threshold of the darkest 1-3 % of observations, without considering surface type, results in climatologically derived values for reflectance that are too dark at bright (urbanized) surface sites. Tilstra et al (2017) suggested that selecting the mode of the RCR histogram improves the characterization of surface reflectance of bright surfaces compared with selecting the minimum values of the RCR. Choosing different thresholds for various surface types may improve the accuracy of retrievals over sites that have high surface reflectance.…”

Section: Regional Performancementioning

confidence: 99%

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

et al. 2018

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Abstract. The Geostationary Ocean Color Imager (GOCI) Yonsei aerosol retrieval (YAER) version 1 algorithm was developed to retrieve hourly aerosol optical depth at 550 nm (AOD) and other subsidiary aerosol optical properties over East Asia. The GOCI YAER AOD had accuracy comparable to ground-based and other satellite-based observations but still had errors because of uncertainties in surface reflectance and simple cloud masking. In addition, near-real-time (NRT) processing was not possible because a monthly database for each year encompassing the day of retrieval was required for the determination of surface reflectance. This study describes the improved GOCI YAER algorithm version 2 (V2) for NRT processing with improved accuracy based on updates to the cloud-masking and surface-reflectance calculations using a multi-year Rayleighcorrected reflectance and wind speed database, and inversion channels for surface conditions. The improved GOCI AOD τ G is closer to that of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD than was the case for AOD from the YAER V1 algorithm. The V2 τ G has a lower median bias and higher ratio within the MODIS expected error range (0.60 for land and 0.71 for ocean) compared with V1 (0.49 for land and 0.62 for ocean) in a validation test against Aerosol Robotic Network (AERONET) AOD τ A from 2011 to 2016. A validation using the Sun-Sky Radiometer Observation Network (SONET) over China shows similar results. The bias of error (τ G − τ A ) is within −0.1 and 0.1, and it is a function of AERONET AOD and Ångström exponent (AE), scattering angle, normalized difference vegetation index (NDVI), cloud fraction and homogeneity of retrieved AOD, and observation time, month, and year. In addition, the diagnostic and prognostic expected error (PEE) of τ G are estimated. The estimated PEE of GOCI V2 AOD is well correlated with the actual error over East Asia, and the GOCI V2 AOD over South Korea has a higher ratio within PEE than that over China and Japan.

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Section: Regional Performancementioning

confidence: 99%

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

et al. 2018

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“…Meteorological information relevant to the retrieval are temperature-pressure profiles and surface pressure, acquired from the European Centre for Medium-Range Weather Forecast (ECMWF) ERA-Interim database (Dee et al, 2011) at the GOME-2A pixel using nearest-neighbour interpolation. Surface albedo is derived using nearest-neighbour interpolation for version 1.3 of GOME-2A LER climatology derived from Tilstra et al (2017), which is at a 1 • × 1 • grid. Typical values of the surface albedo over the region of interest is around 0.21.…”

Section: Demonstration Case: 2010 Russian Wildfiresmentioning

confidence: 99%

Error sources in the retrieval of aerosol information over bright surfaces from satellite measurements in the oxygen A band

et al. 2018

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Abstract. Retrieving aerosol optical thickness and aerosol layer height over a bright surface from measured top-ofatmosphere reflectance spectrum in the oxygen A band is known to be challenging, often resulting in large errors. In certain atmospheric conditions and viewing geometries, a loss of sensitivity to aerosol optical thickness has been reported in the literature. This loss of sensitivity has been attributed to a phenomenon known as critical surface albedo regime, which is a range of surface albedos for which the topof-atmosphere reflectance has minimal sensitivity to aerosol optical thickness. This paper extends the concept of critical surface albedo for aerosol layer height retrievals in the oxygen A band, and discusses its implications. The underlying physics are introduced by analysing the top-ofatmosphere reflectance spectrum as a sum of atmospheric path contribution and surface contribution, obtained using a radiative transfer model. Furthermore, error analysis of an aerosol layer height retrieval algorithm is conducted over dark and bright surfaces to show the dependence on surface reflectance. The analysis shows that the derivative with respect to aerosol layer height of the atmospheric path contribution to the top-of-atmosphere reflectance is opposite in sign to that of the surface contribution -an increase in surface brightness results in a decrease in information content. In the case of aerosol optical thickness, these derivatives are anti-correlated, leading to large retrieval errors in high surface albedo regimes. The consequence of this anti-correlation is demonstrated with measured spectra in the oxygen A band from the GOME-2 instrument on board the Metop-A satellite over the 2010 Russian wildfires incident.

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“…We use the exact illumination and viewing geometry (θ, θ 0 , ϕ − ϕ 0 ) of each individual measurement and colocate MODIS pixels with the GOME-2A pixel centre to obtain the surface BRDF parameters over the scene. For Lambertian cloud fractions, we use the GOME-2A surface LER value from Tilstra et al (2017). To simulate the measured reflectance, we assume the geometric 5 cloud fraction distribution shown in Fig.…”

Section: Gome-2a Cloud Fraction Simulationsmentioning

confidence: 99%

“…monthly 25 climatologies), a large ensemble of measurements taken over a scene over multiple years is analyzed statistically, and based on the lower 1% percentile reflectance, an inversion is done to retrieve the surface reflectance (e.g. Koelemeijer et al (2003), Kleipool et al (2008), Tilstra et al (2017)). Depending on the exact viewing and illumination geometry however, the surface may appear darker or brighter.…”

Section: Introductionmentioning

confidence: 99%

“…That measurements taken under different viewing geometry configurations are used to create the climatologies, does not mean that climatological surface LER values are representative for typical or average viewing geometries of the instruments. Figure 1a, b show the minimum surface LER climatology (2007)(2008)(2009)(2010)(2011)(2012)(2013) at 494 nm and 772 nm for March over Amazonia for GOME-2 onboard MetOp-A (hereafter GOME-2A) from Tilstra et al (2017). This climatology was derived from the 1% 20 cumulative reflectances gathered irrespective of viewing geometry.…”

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confidence: 99%

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The importance of surface reflectance anisotropy for cloud and NO<sub>2</sub> retrievals from GOME-2 and OMI

Lorente¹,

Boersma²,

Stammes³

et al. 2018

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Abstract. The angular distribution of the light reflected by the Earth's surface influences top-of-atmosphere (TOA) reflectance values. This surface reflectance anisotropy has implications for UV/Vis satellite retrievals of albedo, clouds, and trace gases such as nitrogen dioxide (NO2). These retrievals routinely assume the surface to reflect light isotropically. Here we show that cloud fractions retrieved from GOME-2A and OMI with the FRESCO and OMCLDO2 algorithms have an East-West bias of 10 % to 50 % over rugged terrain, and that this bias originates from the assumption of isotropic surface reflection. To interpret the across-track bias with the DAK radiative transfer model, we implement the Bidirectional Reflectance Distribution Function (BRDF) from the Ross-Li semi-empirical model. Testing our implementation against state-of-art RTMs LIDORT and SCIATRAN, we find that simulated TOA reflectance generally agrees to within 1 %. By replacing the assumption of isotropic surface reflection in the cloud retrievals over vegetated scenes with scattering kernels and corresponding BRDF parameters from a daily, high-resolution database derived from 16 years' worth of MODIS measurements, the East-West bias in the retrieved cloud fractions largely vanishes. We conclude that across-track biases in cloud fractions can be explained by cloud algorithms not adequately accounting for the effects of surface reflectance anisotropy. The implications for NO2 air mass factor (AMF) calculations are substantial. Under moderately polluted NO2 and backscatter conditions, clear-sky AMFs are up to 20 % higher and cloud radiance fractions up to 40 % lower if surface anisotropic reflection is accounted for. The combined effect of these changes is that NO2 total AMFs increase by up to 30 % for backscatter geometries (and decrease by up to 35 % for forward scattering geometries), stronger than the effect of either contribution alone. In an unpolluted troposphere, surface BRDF effects on cloud fraction counteract (and largely cancel) the effect on the clear-sky AMF. Our results emphasize that surface reflectance anisotropy needs to be taken into account in a coherent manner for more realistic and accurate retrievals of clouds and NO2 from UV/Vis satellite sensors. These improvements will be beneficial for current sensors, in particular for the recently launched TROPOMI instrument with a high spatial resolution.

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Surface reflectivity climatologies from UV to NIR determined from Earth observations by GOME‐2 and SCIAMACHY

Cited by 99 publications

References 60 publications

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

Error sources in the retrieval of aerosol information over bright surfaces from satellite measurements in the oxygen A band

The importance of surface reflectance anisotropy for cloud and NO<sub>2</sub> retrievals from GOME-2 and OMI

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Surface reflectivity climatologies from UV to NIR determined from Earth observations by GOME‐2 and SCIAMACHY

Cited by 99 publications

References 60 publications

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

Error sources in the retrieval of aerosol information over bright surfaces from satellite measurements in the oxygen A band

The importance of surface reflectance anisotropy for cloud and NO&lt;sub&gt;2&lt;/sub&gt; retrievals from GOME-2 and OMI

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The importance of surface reflectance anisotropy for cloud and NO<sub>2</sub> retrievals from GOME-2 and OMI